Time-Frequency Spectral Signature of Limb Movements and Height Estimation Using Micro-Doppler Millimeter-Wave Radar

Balal, Yael; Balal, Nezah; Richter, Yair; Pinhasi, Y.

doi:10.3390/s20174660

Cited by 13 publications

(12 citation statements)

References 24 publications

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“…Lastly, MMW radars have been adopted to monitor animal movements [64]. Animal trajectories are more variable than compared vehicle trajectories, increasing the difficulty of identifying the data errors.…”

Section: Future Researchmentioning

confidence: 99%

Identifying and correcting the errors of vehicle trajectories from roadside millimetre‐wave radars

Lei

Zhao

et al. 2022

IET Intelligent Trans Sys

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Millimetre‐wave (MMW) radars have been increasingly deployed along the roadside highways to collect vehicle trajectory data, which are valuable for traffic safety analyses and traffic control decisions. Nevertheless, possible errors of the trajectories resulting from roadside MMW radars have not been well documented. This study scrutinizes roadside MMW radar data and the concurrent video ground truth to identify five typical errors of vehicle trajectories, including different vehicles tagged with the same ID, different trajectories produced by the same vehicle, ghost vehicles, erroneous vehicle classification, and vehicle location drift. Data cleaning methods were developed to identify and correct these errors. The performance of the developed methods was evaluated empirically in a case study based on the data of five radars, which were installed at different locations along the highway and had different headings. The thresholds of the developed method were calibrated using one radar data and then applied to the data of the five radars. The results showed that the data qualities of all radars are greatly improved by the developed methods, demonstrating that the developed methods are generally applicable for cleaning vehicle trajectory data from roadside MMW radars.

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Section: Future Researchmentioning

confidence: 99%

Identifying and correcting the errors of vehicle trajectories from roadside millimetre‐wave radars

Lei

Zhao

et al. 2022

IET Intelligent Trans Sys

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“…WiGig is a key technology for high-bandwidth, high-rate communication based LANs and backbone networks, with WiGig messaging rates of up to gigabits. Real-time motion recognition features can be extracted efficiently, and features are extracted from micro-Doppler features rather than traditional signal amplitude features, thus improving feature accuracy [25] .The results show that using a CNN two-channel deep neural network with micro-Doppler features as input after parameter estimation and image pre-processing of the target signal increases the network depth and is effective in classification [26][27] .…”

Section: Related Workmentioning

confidence: 99%

Millimeter wave radar-based method for detecting typical dangerous driving manoeuvres

Hao

Dang

et al. 2022

Preprint

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In response to the dangerous driving behaviour prevalent in current car drivers, there is a need to accurately warn and correct the driver's driving behaviour in real time in a small, mobile, enclosed space. This paper proposes a method of hazardous action detection based on Millimeter wave radar. A millimeter wave radar chip is installed in the cab and the collected FMCW differential frequency signal is Fourier transformed to obtain the IF signal, followed by the target action micro Doppler eigen spectrum as the eigen value. By parsing the matrix signal intensity of the signal echo model of the input channel, the synthesised waveform passing through the mixer is fed into a multi-layer filter for spurious filtering, and the lateral fusion method using the optimised deep learning SlowFast CNN is used to estimate and classify the feature vector against each action. In this paper, nine typical dangerous driving manoeuvres are experimentally set up and the accuracy of the classification results of the self-created dataset is analysed, verifying that the method is highly robust and the accuracy of the target manoeuvre recognition can reach up to 97%.

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“…Identifying and locating small moving items has always been a challenge due to numerous physical factors. There is a concrete need to detect and localize small targets (i.e., in the order of a few millimeters) at a range resolution of a few centimeters, such as with moving vehicles [1], where it is necessary to detect the locations of pedestrians or other vehicles; gesture detection [2,3] in order to detect the motion characteristics of a target or translate movements into a human-machine interface; target localization; searching for survivors buried in rubble [4][5][6]; road safety [7]; transient sensors [8] that are realized mainly by optical systems; and other applications [9]. Radars with millimeter wavelength (MMW) capability have become more common in the military [10], civilian, and automotive [11] industries.…”

Section: Introductionmentioning

confidence: 99%

“…Over time, technology has progressed, and the requirement to protect private and public areas against new targets and scenarios that were not detectable in the past has become more feasible. As a result of such developments, the radar industry has seen great progress, mainly due to increased transmission frequencies from the S-band (2)(3)(4) to the W-band (75-110 GHz) and improved signal processing abilities. The increase in transmitting frequencies has enabled the detection of stealthy and fast targets, as a result of the reduced transmission wavelength that increases scattering resistance of targets [12], such as bullets [13,14], drone blades [15][16][17][18], missiles, and birds [19].…”

Section: Introductionmentioning

confidence: 99%

Tracking of Evasive Objects Using Bistatic Doppler Radar Operating in the Millimeter Wave Regime

et al. 2022

Self Cite

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In this study, we propose a range detection (RD) ability by a continuous wave (CW) bistatic Doppler radar (RDCWB) of small and fast targets with very high range resolution. The target’s range and velocity are detected simultaneously. The scheme is based on the transmission of a continuous wave (CW) at millimeter wavelength (MMW) and the measurement of the respective Doppler shifts associated with target movements in different directions. The range resolution in this method is determined by the Doppler resolution only, without the necessity to transmit the modulated waveforms as in frequency modulation continuous wave (FMCW) or pulse radars. As the Doppler resolution in CW depends only on the time window required for processing, a very highrange resolution can be obtained. Most other systems that perform target localization use the transmission of wide-band waveforms while measuring the delay of the received signal scattered from the target. In the proposed scheme, the range resolution depends on the processed integration time of the detected signal and the velocity of the target. The transmission is performed from separated antennas and received by a single antenna. The received signal is heterodyned with a sample of the transmitted signal in order to obtain the Doppler shifts associated with the target’s movement. As in a multi-in multi-out (MIMO) configuration, the presented scheme allows for the accumulation of additional information for target classification. Data on the target’s velocity, distance, direction, and instantaneous velocity can be extracted. Using digital processing, with the additional information obtained by analyzing the difference between the resulting intermediate frequencies caused by the Doppler effect, it is possible to calculate the distance between the radar and the target at high resolution in real-time. The presented method, which was tested experimentally, proved to be highly effective, as only one receiver is required for the detection, while the transmission is carried out using a fixed, single-frequency transmission.

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Time-Frequency Spectral Signature of Limb Movements and Height Estimation Using Micro-Doppler Millimeter-Wave Radar

Cited by 13 publications

References 24 publications

Identifying and correcting the errors of vehicle trajectories from roadside millimetre‐wave radars

Identifying and correcting the errors of vehicle trajectories from roadside millimetre‐wave radars

Millimeter wave radar-based method for detecting typical dangerous driving manoeuvres

Tracking of Evasive Objects Using Bistatic Doppler Radar Operating in the Millimeter Wave Regime

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